Data vs. Commerce

Every company that sells online wants frictionless commerce. But what does that actually look like in practice? Most businesses have already figured out the #1 rule of cutting out friction in their physical supply chain: all of its parts need to talk to each other, and somebody needs to own it. ㅤ Far fewer have figured that out for their digital supply chains. And somewhere in that pile of disconnected projects, data and commerce stopped talking. The most expensive relationship in your business needs work. This is their standing appointment. This is Data vs. Commerce. ㅤ Each week, hosts Matt Johnson and Floyd Blaikie sit down with the people who own the data, run the platforms, and pay the price when those two stop playing nice. ㅤ If you're responsible for any part of how products get from a database to a doorstep, this is your show. New episodes drop weekly. Subscribe on Apple, Spotify, or wherever you listen.

Data vs. Commerce is a business podcast hosted by Unknown Host, with 0 episodes on record and a Required Pod Score of 80.

Unknown Host hosts Data vs. Commerce, a business show with 0 episodes published.

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  Episode #19

  Why only 5% of distributors call e-commerce their top channel | Ep. 19

  Sep 30, 202629 min

  Distributors think their customers want reps who know the catalog. Their customers mostly want to know where their stuff is. ㅤ Floyd Blaikie brings Matt Johnson a report she got lost in the morning they recorded. It's the State of Sales in Distribution 2026 from Phocas , built on a survey of 100 distributors. Floyd reads the numbers. Matt, who leads go-to-market for distribution at Pivotree , explains why they look the way they do. ㅤ This is a hosts-only episode, so nobody takes a side. The friction comes from the data itself. Then Matt admits that his own sales agent hallucinated on unmaintained data, and his step one for distributors has nothing to do with buying AI. ㅤ What We Cover Why 34% of distributors don't measure new business win rate, and 39% don't track or don't know their forecast accuracy Why the data used by the most people inside a distributor gets dirty the fastest 75% of distributors prize rep product knowledge, while about 5% say their customers want technical expertise What happens when product knowledge lives in reps' heads instead of the product database Why electrical buyers care about fast quotes and "where's my order?" more than discovery 55% of distributors call field sales their top channel and 5% say e-commerce, which Matt reads as being in the retention business more than the growth business Every distributor with no plans to adopt AI has a sales team under 20 reps Matt's step one: get the people who maintain the data into the strategic meetings ㅤ Resources Mentioned State of Sales in Distribution 2026 , from Phocas Data vs. Commerce Episode 6 with Tromml Amazon Business Pivotree

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  Episode #18

  The sell sheet that turned into a product data audit | David Feinleib, Founder and CEO, It'sRapid | Ep. 18

  Sep 23, 202623 min

  Sell sheets, spec sheets and price books are the collateral B2B salespeople say they can't work without, and they go out of date the moment the catalog changes. Some manufacturers can only refresh a price book every 12 to 18 months. ㅤ Matt Johnson and Floyd Blaikie of Pivotree sat down with David Feinleib , founder and CEO of It'sRapid, which generates marketing collateral straight from PIM data. David took the data side. His argument: when you produce thousands of documents from product data, every missing spec or diagram turns into a gap on the page. One customer told him it felt like an audit for their data. ㅤ Matt pushed on governance and why stale data gets shipped anyway. Floyd asked whether the gaps turn into finger pointing. David recast the fight as data versus design, the left brain and right brain of every sell sheet. ㅤ Guest Bio David Feinleib is founder and CEO of It'sRapid, a Palo Alto company that turns product data from PIM and DAM systems into sell sheets, price books, product images and retail media ads. Before It'sRapid he founded Content Analytics, an ecommerce analytics and product content platform that Syndigo acquired in 2019. He's a former general partner at Mohr Davidow Ventures, led product management teams at Microsoft, and wrote Why Startups Fail . He took the data side on this episode. ㅤ What We Cover Why a customer told David, "I didn't realize I bought an audit tool," and what the gaps on the page revealed about their product data Price books that can only be updated every 12 to 18 months because the data keeps changing on the team The legacy workflow: custom scripts pushing data into templates, then sign off from marketing and legal Regenerating 10,000 pieces of collateral after a logo change, and what happens when one field flows through to 10,000 sheets Matt's case that the problem often happens in governance: letting stale data in and shipping it anyway Webhooks as the ideal state, where a data change triggers regenerated sheets automatically Data and design as David's version of data vs. commerce, and the client who went from one region to seven or eight Whether print is dead, tested against a printed birthday card versus an e-card ㅤ Resources Mentioned It'sRapid (David's email: dave@itsrapid.ai ) Pivotree Intelligent Managed Services for Stibo STEP customers, including the free Intelligent Health Check Stibo Systems STEP Data vs. Commerce Episode 6 (field sales and AI)

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  Episode #16

  The chargeback you don't see until six months later | Leo Rodriguez, Vice President, River Plate, Inc. | Ep. 16

  Sep 9, 202632 min

  A packaging change is a data change, and most brands don't treat it as one. An item profile gets built months in advance, pitched to a retailer as a 12-pack, and then a new channel wants six or eight to a box. Nobody updates the profile. The POs arrive at the warehouse and the unit measures don't marry. ㅤ Matt Johnson and Floyd Blaikie sit down with Leo Rodriguez , VP at River Plate, Inc., a 3PL in Southern California. Leo took the data side, which is a more interesting position than it sounds coming from the person whose team physically touches the box. ㅤ Floyd opens by reading a line from his LinkedIn back to Matt: the operation is only as clean as the data behind it. What follows is what that looks like at the end of the chain. Bad mapping that shows a green check mark. ASNs going out wrong for two weeks. Chargebacks that surface six months later, when corrective action is no longer an option. ㅤ Guest Bio Leo Rodriguez is Vice President at River Plate, Inc., a family-owned third-party logistics provider in Simi Valley, California. The business started as local trucking and freight in Los Angeles and now runs a 138,000 square foot facility covering e-commerce fulfillment, wholesale and retail distribution, value-added services, cross-docking, and freight logistics. Leo has been there 17 years, going into his eighteenth. He joined without a supply chain background, took courses online, and learned the operation on the floor. ㅤ What We Cover Why the item profile and the purchase order stop matching, and why the 3PL is the last to know What happens when a brand sets up a 12-pack, then sells the same SKU as a six-pack on a different channel Why a green check mark on an integration doesn't mean the data arrived correctly downstream The mapping change on a tech provider's back end that nobody communicated until Target sent an email Chargebacks that don't surface until six months after the mistake, and what that does to corrective action The onboarding red flag: five bullet points sent to a brand, two of them answered Pre-mapped connections to Walmart Marketplace and Shopify versus a REST API and what each one lets you control The reverse logistics client who built his own returns dashboard with Claude and connected it in 15 minutes Leo's advice: test the reality, not the PowerPoint, and go past go live before you believe it works ㅤ Resources Mentioned ㅤ River Plate, Inc. (RFQ page and discovery call) Walmart Marketplace Shopify Claude Target Kohl's Amazon Salesforce

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  Episode #15

  Lift and shift is the wrong goal for a PIM move | Jay Roxe, Chief Marketing Officer, inriver | Willem Van Dijk, Director of Data Platforms & Services, Pivotree | Ep. 15

  Sep 2, 202628 min

  Everyone agrees the legacy PIM has hit its ceiling. Almost nobody wants to own the move off it. ㅤ In the third and final part of the PIM series, Matt Johnson and Floyd Blaikie get into the part the first two episodes set up. How do you actually move? Jay Roxe of inriver and Willem Van Dijk of Pivotree both argue the move has gotten faster and cheaper in the space of a year, because AI now sits at every stage of the delivery cycle rather than one. ㅤ That creates a different problem. If standing up a platform that looks like your old one is now easy, the temptation to lift and shift gets stronger exactly when it's least appropriate. Jay's position is that B2B buyers are already researching and selecting with AI, so a migration that preserves the old model preserves the old invisibility. Floyd pushes on the credibility question from the buyer's chair, and Matt names the valley of pain in the middle of every implementation where the schedule quietly goes. ㅤ Guests Jay Roxe is Chief Marketing Officer at inriver, the product data platform. He came up through software engineering and product before marketing leadership roles at Microsoft, GE, athenahealth, Rapid7, and BitSight, and most recently HYPR. He lives near the Boston Marathon course and has run it. ㅤ Willem Van Dijk is Director of Data Platforms & Services at Pivotree . He has spent close to twenty years in master data management and PIM, including multiple leadership roles at Stibo Systems where he opened the retail vertical and later the Australia and New Zealand business. ㅤ What We Cover Where AI now sits in a PIM delivery cycle: needs gathering, user stories and the grooming of them, build instructions, test scripts written as the build happens, and post-delivery support triage. Why Pivotree can quote fixed bid now, and what has to be true about delivery for that to work. Why writing user stories with AI surfaces the workflow that actually exists, including the Tuesday afternoon email from Bob to Mary that no system has ever recorded. Jay on lift and shift as the wrong goal, and what happens to discoverability if you carry the old model across. The line between free content and governed, on-brand, compliant, regulatory approved content, and why only one of them needs infrastructure. Floyd on the person who has been burned before, and what has actually changed since their last migration went sideways. Jay's customer survey: about 65 customers, more than two-thirds cutting product data maintenance time by more than 30%, and 75% launching faster. The valley of pain in the middle of every implementation, requirements bloat when the light bulbs go off, and what a good SI does about both. ㅤ Resources Mentioned inriver product data maturity research, covering more than 400 manufacturers and distributors Pivotree LinkedIn Live event with Jay and Willem, Wednesday September 2nd, linked from the Data vs. Commerce, Pivotree, and inriver LinkedIn pages MCPs, referenced by Jay on moving prototypes into production Contact: jay.roxe@inriver.com and willem@pivotree.com

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  Episode #13

  Enriching product data with AI: Does it actually save time? | Ep. 13

  Aug 19, 202622 min

  Product data enrichment is one of the most manually intensive jobs in commerce, and in B2B distribution it scales badly: hundreds of supplier feeds, a million-plus part numbers, and a normalization standard that varies category to category and supplier to supplier. Which is why every vendor now claims to fix it with AI. ㅤ Matt Johnson and Floyd Blaikie take opposite ends of that claim in this solo episode of Data vs. Commerce, the show from Pivotree . Floyd takes the data side, worried about AI being confidently incorrect when your ability to sell depends on the data being right. Matt takes the commerce side, having started his career copy and pasting supplier data out of sales sheets into spreadsheets and ultimately into a PIM. ㅤ Then Matt tells the story that complicates both of their positions: a large national distributor ran an AI enrichment tool built for industrial supply against Claude, which isn't marketed as a product data enrichment tool at all. Claude killed it, and it wasn't even close. The reason came back to what made product data teams successful 20 years ago. ㅤ What We Cover Why the gap between the MSCs and Amazon B2Bs of the world and mid-market distributors is a data labor gap, not a technology gap ㅤ The customer line Matt can't shake: enrichment is table stakes, enrichment is a commodity, PIMs do it, offshore teams do it, even the birds and bees do it ㅤ Why enrichment experiments come back as a total cost of ownership wash, with QA and correction time replacing the time saved ㅤ The head-to-head test where Claude beat a purpose-built industrial supply enrichment tool, and what the winning team did first ㅤ The documentation that made it work: a defined set of attributes and acceptable values, tier one attributes marked required, and a task that fails if the model deviates ㅤ Floyd's argument with her seven-year-old about Gemini and the ice cream store, and the magic wand problem it exposes ㅤ Why distributors doing hundreds of millions in sales stall out when two internal groups can't agree how to standardize a part type ㅤ Why the enrichment conversation starts with your director of sales and head of e-commerce, reverse engineering context from the downstream channels

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